﻿* Encoding: UTF-8.
*************************************<<<Don't forget to turn on the filter! >>**********************************************


**Creating filter 

****Filtering by "usable" 
1=Usable 
2= duplicate IPs (cell highlighted in pink, kept one of the duplicated IPs with the longest duration if it's completed)
3= Incomplete responses ("Finished" =0) and terminated responses (fail the term logic we set up)
4= Age (younger than 18 years old)
5= failed both Q27_7 and Q65_6 --> 2 is the correct answer for both embedded attention check questions
6= Speedsters (duration was less than 300s)

***********Note that determing speedsters are judgement call, so feel free to include or exclude responses if you have your own criteria!!!!!
***********Note there are some overlaps in filter criteria (e.g., duplicate IPs can also be incomplete answers).


                                                                                  
USE ALL.
COMPUTE filter_$=(Usable = 1).
VARIABLE LABELS filter_$ 'Usable = 1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.


* Check usable data (Total N = 1573  usable =1003)

freq Usable.


* Check frequency of demographics
Q5=Gender (1Male 2Female)
age(originally Q6)=Age
Q7=Hispanic
Q8=Race (multivalue)
Q9=Education
income(originally Q10)=Income

freq Q5 age Q7 Q9 income


**************************************************************************************


* Check Quota fulfilment

***** recode AGE  (AGE_GR: 1 = 18-29; 2=30-49; 3= 50-64; 4= 65+)

IF (age>17) AND (age<30) AGE_GR=1.
IF (age>29) AND (age<50) AGE_GR=2.
IF (age>49) AND (age<65) AGE_GR=3.
IF (age=65) OR (age>65) AGE_GR=4.

freq AGE_GR


***** recode EDU (EDU_GR: 1= High school graduate or less, 2 = Some college / Associate degree, 3= College graduate or more)


IF (Q9=1) OR (Q9=2) EDU_GR=1.
IF (Q9=3) OR (Q9=4) EDU_GR=2.
IF (Q9=5) OR (Q9=6) OR (Q9=7) OR (Q9=8) EDU_GR=3.

freq EDU_GR.


**** recode Income (INC_GR: 1=less than $30K, 2=$30K to $49,999, 3=$50K to $74,999, 4=$75K and up)


IF (income=1) INC_GR=1.
IF (income=2) INC_GR=2.
IF (income=3) INC_GR=3.
IF (income=4) OR (income=5) OR (income=6) INC_GR=4.

freq INC_GR

**** recode race
Since we have multivalue Race question, we recoded race in the excel first
You will see a column named RC where we recode those who choose two or more races into others, see below:
In excel spreadsheet, click Q8 > sort and filter > uncheck 1,2,3,4,5,6. Then, you’ll see answers with more than one choice. You can highlight those blocks (yellow)
Create a new column (RC) next to Q8. Clear filter on Q8. Copy Q8 to the new column(RC)
Create filter on RC, create filter and select by color (yellow). Put 7 on those yellow rows and remove the values (e.g., 3,5)
Clear the filter on RC. You now have a new race column with answers from 1 to 5, 6 = others, 7 = multiple answers


**** We then combine 6 = others and 7 =multiple answers in SPSS
  

RECODE RC ('1'=1) ('2'=2) ('3'=3) ('4'=4) ('5'=5) (ELSE=6) INTO RaceRC.
VARIABLE LABELS  RaceRC 'RaceRecode'.
EXECUTE.

freq RaceRC
    
****recode RaceRC
White
Black or African-American
Asian or Asian-American
Other

IF (RaceRC=5) Race_quota=1.
IF (RaceRC=3) Race_quota=2.
IF (RaceRC=2) Race_quota=3.
IF (RaceRC=1) OR (RaceRC=4) OR (RaceRC=6)  OR (RaceRC=7) Race_quota=4.

freq  Race_quota
    

****Check reliability of the news media trust scale (Q24_1 Q24_2 Q24_3 Q24_4 Q24_5) and create an index, Cronbach’s α = .94

RELIABILITY
  /VARIABLES=Q24_1 Q24_2 Q24_3 Q24_4 Q24_5
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS.

COMPUTE GeneralMediaTrust=mean(Q24_1,Q24_2,Q24_3,Q24_4,Q24_5).
VARIABLE LABELS  GeneralMediaTrust 'GeneralMediaTrust'.
EXECUTE.

FREQUENCIES VARIABLES=GeneralMediaTrust
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

****Check reliability of the social media attitude scale (Q55_1 Q55_2 Q55_3 Q55_4) and create an index, Cronbach’s α = .87

RELIABILITY
  /VARIABLES=Q55_1 Q55_2 Q55_3 Q55_4
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS.

COMPUTE SocialMediaAttitudes=mean(Q55_1,Q55_2,Q55_3,Q55_4).
VARIABLE LABELS  SocialMediaAttitudes 'SocialMediaAttitudes'.
EXECUTE.

FREQUENCIES VARIABLES=SocialMediaAttitudes
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

****Check reliability of the social media use scale (from Q31_1 to Q32_11) and create an indix

RELIABILITY
  /VARIABLES=Q32_1 Q32_2 Q32_3 Q32_4 Q32_5 Q32_6 Q32_7 Q32_8 Q32_9 Q32_10 Q32_11
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS VARIANCE.

COMPUTE SocialMediaUse=mean(Q32_1,Q32_2,Q32_3,Q32_4,Q32_5,Q32_6,Q32_7,Q32_8,Q32_9,Q32_10,Q32_11).
VARIABLE LABELS  SocialMediaUse 'SocialMediaUse'.
EXECUTE.

FREQUENCIES VARIABLES=SocialMediaUse
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

****Check reliability of the algorithm reliance scale (Q39_7 Q39_8) and create an indix, Spearman-Brown = .90

RELIABILITY
  /VARIABLES=Q39_7 Q39_8
  /SCALE('ALL VARIABLES') ALL
  /MODEL=SPLIT
  /STATISTICS=SCALE
  /SUMMARY=MEANS VARIANCE.

COMPUTE algoreliance=mean(Q39_7,Q39_8).
VARIABLE LABELS  algoreliance 'algoreliance'.
EXECUTE.

DESCRIPTIVES VARIABLES=algoreliance
  /STATISTICS=MEAN STDDEV MIN MAX KURTOSIS SKEWNESS.

***Check reliability of the perceived efficacy of fact-checking labels (two items for each label)

*Check reliability of the algorithmic label (Q68_2 Q69_2) and create an indix, Spearman-Brown = .77

RELIABILITY
  /VARIABLES=Q68_2 Q69_2
  /SCALE('ALL VARIABLES') ALL
  /MODEL=SPLIT
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS VARIANCE.

COMPUTE AlgorithmLabel=mean(Q68_2,Q69_2).
VARIABLE LABELS  AlgorithmLabel 'AlgorithmLabel'.
EXECUTE.

FREQUENCIES VARIABLES=AlgorithmLabel
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

*Check reliability of the fact-check label (Q68_1 Q69_1) and create an indix, Spearman-Brown = .79

RELIABILITY
  /VARIABLES=Q68_1 Q69_1
  /SCALE('ALL VARIABLES') ALL
  /MODEL=SPLIT
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS VARIANCE.

COMPUTE FactCheckLabel=mean(Q68_1,Q69_1).
VARIABLE LABELS  FactCheckLabel 'FactCheckLabel'.
EXECUTE.

FREQUENCIES VARIABLES=FactCheckLabel
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

*Check reliability of the user label (Q68_3 Q69_3) and create an indix, Spearman-Brown = .81

RELIABILITY
  /VARIABLES=Q68_3 Q69_3
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS VARIANCE.

COMPUTE UserLabel=mean(Q68_3,Q69_3).
VARIABLE LABELS  UserLabel 'UserLabel'.
EXECUTE.

FREQUENCIES VARIABLES=UserLabel
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

*Check reliability of the media label (Q68_4 Q69_4) and create an indix, Spearman-Brown = .79

RELIABILITY
  /VARIABLES=Q68_4 Q69_4
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA
  /STATISTICS=DESCRIPTIVE SCALE
  /SUMMARY=TOTAL MEANS VARIANCE.

COMPUTE MediaLabel=mean(Q68_4,Q69_4).
VARIABLE LABELS  MediaLabel 'MediaLabel'.
EXECUTE.

FREQUENCIES VARIABLES=MediaLabel
  /STATISTICS=STDDEV RANGE MINIMUM MAXIMUM SEMEAN MEAN MEDIAN MODE SKEWNESS SESKEW
  /ORDER=ANALYSIS.

*Recode prior exposure to fact-checking labels (Q67) (remove 8 =Not Sure)

FREQUENCIES VARIABLES=Q67
  /HISTOGRAM
  /ORDER=ANALYSIS.

RECODE Q67 (1=1) (2=2) (3=3) (4=4) (5=5) (6=6) (7=7) (SYSMIS=SYSMIS) (8=SYSMIS) INTO 
    past.
VARIABLE LABELS  past 'past'.
EXECUTE.

DESCRIPTIVES VARIABLES=past
  /STATISTICS=MEAN STDDEV MIN MAX KURTOSIS SKEWNESS.

*Dummy coding party (partyID=Q11; after dummy coding, ID_1=Democratic; ID_2=Republican; ID_3=Independent)

RECODE Q11 (1=1) (2=1) (3=1) (4=2) (5=0) (6=0) (7=0) (SYSMIS=SYSMIS) INTO partyIDdem0rep1ind2.
VARIABLE LABELS  partyIDdem0rep1ind2 'partyIDdem0rep1ind2'.
EXECUTE.

SPSSINC CREATE DUMMIES VARIABLE=partyIDdem0rep1ind2 
ROOTNAME1=ID 
/OPTIONS ORDER=A USEVALUELABELS=YES USEML=YES OMITFIRST=NO.


*Compute interaction terms

COMPUTE pastxsmatt=past * SocialMediaAttitudes.
VARIABLE LABELS  pastxsmatt 'pastxsmatt'.
EXECUTE.

COMPUTE pastxtrust=past * GeneralMediaTrust.
VARIABLE LABELS  pastxtrust 'pastxtrust'.
EXECUTE.

***OLS regression models predicting perceived efficacy of fact-checking labels

*Model 1a

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT AlgorithmLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past.

*Model 1b

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT AlgorithmLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past pastxsmatt.

*Model 2a

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT MediaLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past.

*Model 2b

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT MediaLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past pastxtrust.

*Model 3a

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT FactCheckLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past.

*Model 3b

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT FactCheckLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past pastxtrust.

*Model 4a

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT UserLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past.

*Model 4b

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT UserLabel
  /METHOD=ENTER age ID_2 ID_3 EDU_GR income GeneralMediaTrust SocialMediaAttitudes SocialMediaUse
    algoreliance past pastxsmatt.







